Impact of Cannabis Consumption on Psychotic Symptoms and Reality Monitoring in Patients With Schizophrenia: a Real-life Study

Schizophrenia is a complex psychiatric disorder that affects approximately 1% of the general population, typically emerging in early adulthood. It is characterized by a wide range of symptoms, including positive symptoms (e.g., hallucinations and delusions), negative symptoms (e.g., apathy and emotional blunting), and cognitive impairments. The disorder contributes substantially to the global burden of disease, ranking among the top 30 causes of disability-adjusted life years. Cannabis use is common among individuals with schizophrenia, with estimates suggesting that between 16% and 42% of patients have used cannabis at some point in their lives. The relationship between cannabis use and schizophrenia is multifactorial. Cannabis consumption has been associated with an increased risk of psychosis, worsening of positive symptoms, and earlier onset of the disorder. However, some studies indicate that cannabis may also exert beneficial effects on negative symptoms, potentially mediated by compounds other than THC. Cognitive deficits are a core feature of schizophrenia, particularly impairments in reality monitoring. Reality monitoring refers to the ability to distinguish between internally generated events (intrinsic) and those originating from external sources (extrinsic). In schizophrenia, this capacity is often compromised, leading to misattributions of internal thoughts to external sources, which may contribute to the development of positive symptoms such as hallucinations. Given the dynamic nature of symptoms and substance use in schizophrenia, traditional clinical models may fail to capture the complexity of these interactions. Ecological Momentary Assessment (EMA) provides a novel approach by leveraging mobile devices to collect real-time data on symptoms, substance use, and cognitive performance in everyday life. EMA has been validated in schizophrenia research, offering high-resolution data that can complement and enhance traditional models.